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:orphan: | ||
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.. training/overview.rst: | ||
.. _overview: | ||
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Basic Concepts | ||
============== | ||
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.. important:: Distributed training is not officially supported as of version | ||
|version|; however, some configuration options have worked for nGraph | ||
devices in testing environments. | ||
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Data scientists with locally-scalable rack or cloud-based resources will likely | ||
find it worthwhile to experiment with different modes or variations of | ||
distributed training. Deployments using nGraph Library with supported backends | ||
can be configured to train with data parallelism and will soon work with model | ||
parallelism. Distributing workloads is increasingly important, as more data and | ||
bigger models mean the ability to :doc:`../core/constructing-graphs/distribute-train` | ||
work with larger and larger datasets, or to work with models having many layers | ||
that aren't designed to fit to a single device. | ||
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Distributed training with data parallelism splits the data and each worker | ||
node has the same model; during each iteration, the gradients are aggregated | ||
across all workers with an op that performs "allreduce", and applied to update | ||
the weights. | ||
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Using multiple machines helps to scale and speed up deep learning. With large | ||
mini-batch training, one could train ResNet-50 with Imagenet-1k data to the | ||
*Top 5* classifier in minutes using thousands of CPU nodes. See | ||
`arxiv.org/abs/1709.05011`_. | ||
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.. _arxiv.org/abs/1709.05011: https://arxiv.org/format/1709.05011 |
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